This disclosure relates generally to determining embedding vectors for entities of an online system based on interaction information between the entities of the online system.
Some online systems, such as a social networking system, provides content items to users based on models that attempt to score or rank the content available in the online system based on a likelihood that a user will be interested in the content item. In large online systems, the amount of content available can be very large. In this scenario, processing all or a large portion of the content available to be presented to the user may be unfeasible by the online system. For instance, processing all or a large portion of the content available to be presented to the user may take too much time or too many computing resources. As such, content items that the user might be interested in might be overlooked because the online system does not have enough resources to score or rank those content items.
Embedding vectors can be used to identify content items that a user may be interested in. For instance, if embedding vectors may be generated such that the vectors are correlated to historic interactions between users and content items in the online system. As such, the online system may determine a likelihood of a user being interested in a particular content item based on the distance or angle between the embedding vector of the user and the embedding vector of the content item. However, for large online systems, generating embedding vectors can be a time consuming task. Furthermore, as the size of the entities in the online system increases, the amount of memory used for determining the embedding vectors also increases.